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<article id="content">
<header>
<h1 class="title">Module <code>silk.cli.hpatches_tests</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import os

import numpy as np
import torch
from omegaconf import DictConfig
from silk.cli.image_pair_visualization import img_pair_visual
from silk.config.core import instantiate_and_ensure_is_instance
from silk.datasets.synthetic.primitives import draw_interest_points
from silk.logger import LOG
from silk.metrics.hpatches_metrics import MeanMatchingAccuracy
from silk.transforms.abstract import Transform
from silk.transforms.tensor import Unbatch
from skimage.io import imsave
from torch.utils.data import DataLoader
from tqdm import tqdm


def _tensor_to_python(tensor):
    if len(tensor.shape) &gt; 0:
        return [_tensor_to_python(t) for t in tensor]
    return tensor.item()


def compute_metrics(metric_updates):
    &#34;&#34;&#34;Compute and return all provided metrics.

    Returns
    -------
    dict
        Mapped values of the provided metrics.
    &#34;&#34;&#34;
    return {
        name: _tensor_to_python(metric_update.metric.compute().detach().cpu())
        for name, metric_update in metric_updates.items()
    }


def image_dump(name, image, viz_options):
    path = f&#34;{viz_options.directory}/{name}.png&#34;
    LOG.warning(f&#34;debug dump image to : {path}&#34;)
    imsave(path, image)


def torch_image_to_numpy(image):
    # permute if channel is first
    if image.shape[0] == 1 or image.shape[0] == 3:
        image = image.permute((1, 2, 0))
    image = image.squeeze()
    image = image.detach().cpu().numpy()
    image = (image * 255.0).astype(np.uint8)
    return image.copy()


def gray_image_to_rgb(image):
    image = image.squeeze()
    if len(image.shape) == 3:  # already RGB
        return image
    image = torch.stack((image, image, image), dim=0)
    return image


def vizualize_keypoints(name, image, points, matched_points, viz_options):
    image = torch_image_to_numpy(image)
    points = points.detach().cpu().numpy()

    image = draw_interest_points(
        image,
        points,
        (0, 255, 0),
    )

    if matched_points is not None:
        matched_points = matched_points.detach().cpu().numpy()
        image = draw_interest_points(
            image,
            matched_points,
            (255, 0, 0),
        )

    image_dump(name, image, viz_options)


def visualize_keypoint_matches(
    name,
    image_0,
    image_1,
    matched_points_0,
    matched_points_1,
    good_matches_mask,
    viz_options,
):
    image_0 = torch_image_to_numpy(gray_image_to_rgb(image_0))
    image_1 = torch_image_to_numpy(gray_image_to_rgb(image_1))
    matched_points_0 = matched_points_0.detach().cpu().numpy()
    matched_points_1 = matched_points_1.detach().cpu().numpy()

    image_pair = img_pair_visual(
        image_0,
        image_1,
        matched_points_0,
        matched_points_1,
        good_matches_mask,
    )

    image_dump(name, image_pair, viz_options)


def visualized_probs(name, probs, viz_options):
    probs = torch_image_to_numpy(probs)
    probs = probs.astype(np.float)
    probs -= probs.min()
    probs /= probs.max()
    image_dump(name, probs, viz_options)


def visualize(idx, elem, viz_options):
    if not viz_options.enabled:
        return idx

    os.makedirs(viz_options.directory, exist_ok=True)

    for el in Unbatch(tuple_as_list=True)(elem):
        _visualize_one(idx, el, viz_options)
        idx += 1

    return idx


def _visualize_one(idx, elem, viz_options):
    vizualize_keypoints(
        f&#34;{idx:04d}.keypoints_original&#34;,
        elem[&#34;original_img&#34;],
        elem[&#34;original_points&#34;],
        elem[&#34;matched_original_points&#34;],
        viz_options,
    )
    vizualize_keypoints(
        f&#34;{idx:04d}.keypoints_warped&#34;,
        elem[&#34;warped_img&#34;],
        elem[&#34;warped_points&#34;],
        elem[&#34;matched_warped_points&#34;],
        viz_options,
    )
    visualize_keypoint_matches(
        f&#34;{idx:04d}.keypoints_raw_matches&#34;,
        elem[&#34;original_img&#34;],
        elem[&#34;warped_img&#34;],
        elem[&#34;matched_original_points&#34;],
        elem[&#34;matched_warped_points&#34;],
        good_matches_mask=None,
        viz_options=viz_options,
    )
    visualize_keypoint_matches(
        f&#34;{idx:04d}.keypoints_good_matches&#34;,
        elem[&#34;original_img&#34;],
        elem[&#34;warped_img&#34;],
        elem[&#34;matched_original_points&#34;],
        elem[&#34;matched_warped_points&#34;],
        good_matches_mask=MeanMatchingAccuracy.good_matches_mask(
            elem[&#34;matched_original_points&#34;],
            elem[&#34;matched_warped_points&#34;],
            elem[&#34;homography&#34;].float().to(elem[&#34;matched_original_points&#34;].device),
            threshold=3,
            ordering=&#34;yx&#34;,
        ),
        viz_options=viz_options,
    )

    if &#34;original_probs&#34; in elem.names():
        visualized_probs(
            f&#34;{idx:04d}.original_probs&#34;,
            elem[&#34;original_probs&#34;],
            viz_options=viz_options,
        )
    if &#34;warped_probs&#34; in elem.names():
        visualized_probs(
            f&#34;{idx:04d}.warped_probs&#34;,
            elem[&#34;warped_probs&#34;],
            viz_options=viz_options,
        )


def main(config: DictConfig):
    &#34;&#34;&#34;
    Compute the repeatability and homography estimation metrics for the HPatches dataset.
    &#34;&#34;&#34;
    loader = instantiate_and_ensure_is_instance(config.mode.loader, DataLoader)
    transform = instantiate_and_ensure_is_instance(config.mode.transform, Transform)
    metric_updates = instantiate_and_ensure_is_instance(
        config.mode.metric_updates, DictConfig
    )

    idx = 0
    for elem in tqdm(loader):
        elem = transform(elem)

        idx = visualize(idx, elem, config.mode.visualization)

        for _, metric in metric_updates.items():
            metric(elem)

    metrics = compute_metrics(metric_updates)
    return {
        &#34;metrics&#34;: metrics,
    }</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.cli.hpatches_tests.compute_metrics"><code class="name flex">
<span>def <span class="ident">compute_metrics</span></span>(<span>metric_updates)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute and return all provided metrics.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>dict</code></dt>
<dd>Mapped values of the provided metrics.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def compute_metrics(metric_updates):
    &#34;&#34;&#34;Compute and return all provided metrics.

    Returns
    -------
    dict
        Mapped values of the provided metrics.
    &#34;&#34;&#34;
    return {
        name: _tensor_to_python(metric_update.metric.compute().detach().cpu())
        for name, metric_update in metric_updates.items()
    }</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.gray_image_to_rgb"><code class="name flex">
<span>def <span class="ident">gray_image_to_rgb</span></span>(<span>image)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def gray_image_to_rgb(image):
    image = image.squeeze()
    if len(image.shape) == 3:  # already RGB
        return image
    image = torch.stack((image, image, image), dim=0)
    return image</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.image_dump"><code class="name flex">
<span>def <span class="ident">image_dump</span></span>(<span>name, image, viz_options)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def image_dump(name, image, viz_options):
    path = f&#34;{viz_options.directory}/{name}.png&#34;
    LOG.warning(f&#34;debug dump image to : {path}&#34;)
    imsave(path, image)</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.main"><code class="name flex">
<span>def <span class="ident">main</span></span>(<span>config: omegaconf.dictconfig.DictConfig)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute the repeatability and homography estimation metrics for the HPatches dataset.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def main(config: DictConfig):
    &#34;&#34;&#34;
    Compute the repeatability and homography estimation metrics for the HPatches dataset.
    &#34;&#34;&#34;
    loader = instantiate_and_ensure_is_instance(config.mode.loader, DataLoader)
    transform = instantiate_and_ensure_is_instance(config.mode.transform, Transform)
    metric_updates = instantiate_and_ensure_is_instance(
        config.mode.metric_updates, DictConfig
    )

    idx = 0
    for elem in tqdm(loader):
        elem = transform(elem)

        idx = visualize(idx, elem, config.mode.visualization)

        for _, metric in metric_updates.items():
            metric(elem)

    metrics = compute_metrics(metric_updates)
    return {
        &#34;metrics&#34;: metrics,
    }</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.torch_image_to_numpy"><code class="name flex">
<span>def <span class="ident">torch_image_to_numpy</span></span>(<span>image)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def torch_image_to_numpy(image):
    # permute if channel is first
    if image.shape[0] == 1 or image.shape[0] == 3:
        image = image.permute((1, 2, 0))
    image = image.squeeze()
    image = image.detach().cpu().numpy()
    image = (image * 255.0).astype(np.uint8)
    return image.copy()</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.visualize"><code class="name flex">
<span>def <span class="ident">visualize</span></span>(<span>idx, elem, viz_options)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def visualize(idx, elem, viz_options):
    if not viz_options.enabled:
        return idx

    os.makedirs(viz_options.directory, exist_ok=True)

    for el in Unbatch(tuple_as_list=True)(elem):
        _visualize_one(idx, el, viz_options)
        idx += 1

    return idx</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.visualize_keypoint_matches"><code class="name flex">
<span>def <span class="ident">visualize_keypoint_matches</span></span>(<span>name, image_0, image_1, matched_points_0, matched_points_1, good_matches_mask, viz_options)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def visualize_keypoint_matches(
    name,
    image_0,
    image_1,
    matched_points_0,
    matched_points_1,
    good_matches_mask,
    viz_options,
):
    image_0 = torch_image_to_numpy(gray_image_to_rgb(image_0))
    image_1 = torch_image_to_numpy(gray_image_to_rgb(image_1))
    matched_points_0 = matched_points_0.detach().cpu().numpy()
    matched_points_1 = matched_points_1.detach().cpu().numpy()

    image_pair = img_pair_visual(
        image_0,
        image_1,
        matched_points_0,
        matched_points_1,
        good_matches_mask,
    )

    image_dump(name, image_pair, viz_options)</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.visualized_probs"><code class="name flex">
<span>def <span class="ident">visualized_probs</span></span>(<span>name, probs, viz_options)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def visualized_probs(name, probs, viz_options):
    probs = torch_image_to_numpy(probs)
    probs = probs.astype(np.float)
    probs -= probs.min()
    probs /= probs.max()
    image_dump(name, probs, viz_options)</code></pre>
</details>
</dd>
<dt id="silk.cli.hpatches_tests.vizualize_keypoints"><code class="name flex">
<span>def <span class="ident">vizualize_keypoints</span></span>(<span>name, image, points, matched_points, viz_options)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def vizualize_keypoints(name, image, points, matched_points, viz_options):
    image = torch_image_to_numpy(image)
    points = points.detach().cpu().numpy()

    image = draw_interest_points(
        image,
        points,
        (0, 255, 0),
    )

    if matched_points is not None:
        matched_points = matched_points.detach().cpu().numpy()
        image = draw_interest_points(
            image,
            matched_points,
            (255, 0, 0),
        )

    image_dump(name, image, viz_options)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.cli" href="index.html">silk.cli</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.cli.hpatches_tests.compute_metrics" href="#silk.cli.hpatches_tests.compute_metrics">compute_metrics</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.gray_image_to_rgb" href="#silk.cli.hpatches_tests.gray_image_to_rgb">gray_image_to_rgb</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.image_dump" href="#silk.cli.hpatches_tests.image_dump">image_dump</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.main" href="#silk.cli.hpatches_tests.main">main</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.torch_image_to_numpy" href="#silk.cli.hpatches_tests.torch_image_to_numpy">torch_image_to_numpy</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.visualize" href="#silk.cli.hpatches_tests.visualize">visualize</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.visualize_keypoint_matches" href="#silk.cli.hpatches_tests.visualize_keypoint_matches">visualize_keypoint_matches</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.visualized_probs" href="#silk.cli.hpatches_tests.visualized_probs">visualized_probs</a></code></li>
<li><code><a title="silk.cli.hpatches_tests.vizualize_keypoints" href="#silk.cli.hpatches_tests.vizualize_keypoints">vizualize_keypoints</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
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